arXiv subjects

Wytze de Vries

Publications and source records attributed to Wytze de Vries.

4 recordsLinked to original sources

arXiv2026

Competitor-aware Race Management for Electric Endurance Racing

Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. This paper proposes a bi-level framework for competitor-aware race management that combines game-theoretic optimal control with reinforcement learning. At the lower level, a multi-agent game-theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision-avoidance constraints inspired by motorsport rules. Using this single-lap problem as the environment, reinforcement learning agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two-agent, 45-lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single-agent, minimum-time approaches.

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arXiv2026

A Sequential Convex Programming Approach to Free-trajectory Minimum-lap-time Optimization of Racing Cars

This paper presents a modeling and optimization framework to compute the minimum-lap-time spatial trajectory and powertrain operation of racing cars in a computationally efficient fashion. Specifically, we first derive a quasi-steady-state model of a racing car, whereby the racing line trajectory is jointly optimized. Next, we frame the minimum-lap-time problem and leverage its mostly convex structure by devising a sequential convex programming solution algorithm. We benchmark our method against off-the-shelf nonlinear programming solvers, showing how it can bring computation time down from a few minutes to a few seconds, paving the way for real-time implementations. Moreover, we compare our results to similarly efficient minimum-curvature racing line optimization methods, showing how a minimum-time-based racing line might lead to 4% faster lap-times. Finally, we showcase our framework for optimal powertrain energy management and we validate the common modeling assumption that the racing line is unaffected by energy limitations, showing that this assumption results in marginal lap-time losses of under 0.1%.

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arXiv2026

A Computationally Efficient and Human Implementable Minimum-lap-time Control Policy for Energy-limited Race Cars

This paper presents a provably optimal, real-time capable energy management policy for race cars that provides simple human-driver-implementable control cues. Specifically, we first formulate the energy-constrained minimum-lap-time control problem via Pontryagin's Minimum Principle (PMP) and derive the optimal policy and costate dynamics using Karush-Kuhn-Tucker (KKT) optimality conditions. We show that the optimal control policy follows a bang-bang structure that is easily implementable by a human driver, eliminating the need for potentially dangerous active throttle pedal overwrites or distracting signals. Moreover, the analytical formulation of the optimal system dynamics allows us to recast the problem as a sequence of boundary-value problems, which can be efficiently solved using root-finding methods. Our results show that our proposed approach can compute the same globally optimal control strategies of existing numerical methods based on direct optimal control, whilst drastically reducing computation time from the order of seconds to milliseconds.

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arXiv2024

A Human-optimized Model Predictive Control Scheme and Extremum Seeking Parameter Estimator for Slip Control of Electric Race Cars

This paper presents a longitudinal slip control system for a rear-wheel-driven electric endurance race car. The control system integrates Model Predictive Control (MPC) with Extremum Seeking Control (ESC) to optimize the traction and regenerative braking performance of the powertrain. The MPC contains an analytical solution which results in a negligible computation time, whilst providing an optimal solution to a multi-objective optimization problem. The ESC algorithm allows continuous estimation of the optimal slip reference without assuming any prior knowledge of the tire dynamics. Finally, the control parameters are determined using a human-driven preference-based optimization algorithm in order to obtain the desired response. Simulation results and comparisons with other methods demonstrate the system's capability to automatically determine and track the optimal slip values, showing stability and performance under varying conditions.

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